Probabilistic Models and Inference

The course will focus on advanced probabilistic inference and modelling techniques, complementing the core 'Probabilistic AI & reasoning' course, and causality. On the inference side, the topics include: representation of uncertainty (Bayesian networks, undirected graphical models, probabilistic programs); exact inference algorithms for Bayesian networks; approximate inference algorithms (importance sampling, metropolis-hastings, particle filtering, variational inference); implementation strategies for probabilistic programs; learning for probabilistic inference; discrete probabilistic programming; types of probabilistic queries; algorithms for causal inference. On the modelling side, the course will include a spectrum of common probabilistic models that are often used in practice. 

The final grade of the course consists of the following components:

• Written Exam (weighting 60%)

• Group Assignments: assignment applying and evaluating modelling methods (weighting 40%)

Final grade calculation = 0.6 * Written Exam + 0.4 * Group Assignments

A passing final grade for the course can only be earned when for all components at least a 5.0 is earned, and the weighted final grade is at least a 5.8.

In case of an insufficient final result, repair options may exist in accordance with Article 17A, Times and number of examinations, sub 1, of the Teaching and Examination Regulations, for:

• Written Exam: Resit opportunity

• Group Assignments: Repair opportunity

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).